Vehicle Segmentation and Tracking in the Presence of Occlusions

Abstract

A novel method is presented for automatically visually monitoring a highway when the camera is relatively low to the ground and on the side of the road. In such a case, occlusion and the perspective effects due to the heights of the vehicles cannot be ignored. Using a single camera, the system automatically detects and tracks feature points throughout the image sequence, estimates the 3D world coordinates of the points on the vehicles, and groups those points together in order to segment and track the individual vehicles. Experimental results on different highways demonstrate the ability of the system to segment and track vehicles even in the presence of severe occlusion and significant perspective changes. By handling perspective effects, the approach overcomes a limitation of commercially available machine vision-based traffic monitoring systems that are used in many intelligent transportation systems (ITS) applications. The researchers are targeting this system as a step toward a next generation ITS sensor for automated traffic analysis.

Vehicle Detection and Tracking Techniques : A Concise Review

“…An automatic unique visual-based expressway surveillance approach for segmenting and tracking vehicles during the image series with existence rigorous occlusion due to low-level floor position of camera on the roadside [52]. In this paper, the particular vehicles are detected, segmented and tracked in image sequence by assembling, bunching and approximating of the 3D world coordinates of vehicle's feature points.…”

Modeling the Estimation Errors of Visual-based Systems Developed for Vehicle Speed Measurement

“…This block, therefore, provides as a result the distance traveled by a vehicle as well as the number of frames that the vehicle took to cover that distance. To ensure temporal consistency, it is preferable that the interval between frames is constant and short, and that there are no sudden changes in the direction of the object [32], [33].…”

Empowering vehicle tracking in a cluttered environment with adaptive cellular automata suitable to intelligent transportation systems

“…Declining to detect and resolve the existence of occlusion may cause surveillance errors, comprising incorrect vehicle count, unfitting tracking of individual vehicles and improper classification of vehicle type on that road section. Yet, occlusion detection and resolving are intrinsically difficult, as they depend on vehicle features that would specify if a certain moving object involves one or more than one vehicle [15,17,34]. If it is the latter case, then these features would have to deliver a foundation for distinguishing which vehicle is which.…”